Automatic generation of statement-response sets from conversational text using natural language processing

ABSTRACT

Systems and methods that access an online networked resource using a locator are disclosed. A first item of content on the networked resource is identified. A trigger rule comprising keywords and a sentiment classifier is accessed. A neural network, including input, hidden, and output layers, is used to assign a sentiment classification to the first item of content. The trigger rule, the sentiment classification, and identified keywords, are used to determine whether response content is to be posted to the online networked resource. In response to determining, using the trigger rule, the assigned sentiment classification, and keywords identified in the first item of content, that response content is to be posted to the online networked resource, the sentiment classification and identified keywords are used to select and/or generate a second item of content, and the second item of content is enabled to be posted to the online networked resource.

INCORPORATION BY REFERENCE TO ANY PRIORITY APPLICATIONS

Any and all applications for which a foreign or domestic priority claimis identified in the Application Data Sheet as filed with the presentapplication are hereby incorporated by reference under 1 CFR 1.57.

BACKGROUND OF THE INVENTION Field of the Invention

The invention relates to natural language processing, and in particular,to utilizing natural language processing in generating responsestatements.

Description of the Related Art

Networked information sharing systems are becoming increasinglyessential for the aggregation and dissemination of information. However,conventional information sharing systems often aggregate and disseminatelow quality information and fail to provide an adequate mechanism foridentifying and correcting such low quality information. Thus, such lowquality information becomes further distributed over networkedresources, disadvantageously utilizing ever more memory and processingresources.

SUMMARY

The following presents a simplified summary of one or more aspects inorder to provide a basic understanding of such aspects. This summary isnot an extensive overview of all contemplated aspects, and is intendedto neither identify key or critical elements of all aspects nordelineate the scope of any or all aspects. Its sole purpose is topresent some concepts of one or more aspects in a simplified form as aprelude to the more detailed description that is presented later.

An aspect of the present disclosure relates to a non-transitorycomputer-readable medium comprising computer-readable instructions whichwhen executed by one or more processors cause said one or moreprocessors to perform a process comprising: access a locator of anonline networked resource; access the online networked resource usingthe locator; identify a first item of content of a first type on theaccessed online networked resource; access a trigger rule, the triggerrule comprising one or more keywords and a sentiment classifier; use aneural network comprising: an input layer, one or more hidden layers,and an output layer, to assign a sentiment classification to the firstitem of content; identify whether the first item of content includes oneor more of the keywords; determine, using the trigger rule, the assignedsentiment classification generated using the neural network and keywordsidentified in the first item of content, whether response content is tobe posted to the online networked resource; at least partly in responseto determining, using the trigger rule, the assigned sentimentclassification generated using the neural network and keywordsidentified in the first item of content, that response content is to beposted to the online networked resource, use a response matrix, theassigned sentiment classification generated using the neural network andkeywords identified in the first item of content to select a second itemof content; enable the selected second item of content to be posted tothe online networked resource.

An aspect of the present disclosure relates to a computer-implementedmethod, the method comprising: accessing, using a computer systemincluding one or more computing devices, a locator of an onlinenetworked resource; accessing, using the computer system, the onlinenetworked resource using the locator; identifying, using the computersystem, a first item of content on the accessed online networkedresource; accessing, using the computer system, a trigger rule, thetrigger rule comprising one or more keywords and a sentiment classifier;using a learning engine to assign a sentiment classification to thefirst item of content; identifying whether the first item of contentincludes one or more of the keywords; determine, using the trigger rule,the assigned sentiment classification generated using the learningengine and keywords identified in the first item of content, whetherresponse content is to be posted to the online networked resource; atleast partly in response to determining, using the trigger rule, theassigned sentiment classification generated using the learning engineand keywords identified in the first item of content, that responsecontent is to be posted to the online networked resource, using theassigned sentiment classification generated using the neural network andkeywords identified in the first item of content to select and/orgenerate a second item of content; and enabling the second item ofcontent to be posted to the online networked resource.

An aspect of the present disclosure relates to a computer systemcomprising: a computing device; a network interface; a non-transitorydata media configured to store instructions that when executed by thecomputing device, cause the computing device to perform operationscomprising: access a locator of an online networked resource; access theonline networked resource using the locator; identify a first item ofcontent on the accessed online networked resource; access a triggerrule, the trigger rule comprising one or more keywords and a sentimentclassifier; use a learning engine to assign a sentiment classificationto the first item of content; identify whether the first item of contentincludes one or more of the keywords; determine, using the trigger rule,the assigned sentiment classification generated using the learningengine and keywords identified in the first item of content, whetherresponse content is to be posted to the online networked resource; atleast partly in response to determining, using the trigger rule, theassigned sentiment classification generated using the learning engineand keywords identified in the first item of content, that responsecontent is to be posted to the online networked resource, use theassigned sentiment classification generated using the neural network andkeywords identified in the first item of content to select and/orgenerate a second item of content; and enable the second item of contentto be posted to the online networked resource.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments will now be described with reference to the drawingssummarized below. Throughout the drawings, reference numbers may bere-used to indicate correspondence between referenced elements. Thedrawings are provided to illustrate example embodiments described hereinand are not intended to limit the scope of the disclosure.

FIG. 1A is a block diagram illustrating an example embodiment of anoperating environment.

FIG. 1B is a block diagram illustrating an embodiment of examplecomponents of a content monitoring and response system.

FIG. 2 illustrates an example natural language processing engine.

FIG. 3A illustrates an example supervised learning model environment.

FIG. 3B illustrates an example neural network architecture.

FIG. 4 illustrates an example process.

FIG. 5 illustrates an example response selection matrix.

FIGS. 6 and 7 illustrate example processes.

FIGS. 8A-8E illustrate example user interfaces.

DETAILED DESCRIPTION

With the increased usage of computing networks and communicationplatforms, there has been a resultant increase in statement andcommunication on disparate communication platforms, such as socialnetworking platforms. However, many such statements may be inaccurate,offensive, or misleading. Conventional systems are unable to detect,identify, and respond to such statements. Thus, such low qualityinformation becomes further distributed over networked resources,disadvantageously utilizing ever more memory and processing resources.

An aspect of the present disclosure relates to methods and systems thatutilize natural language processing implemented using a natural languageprocessing engine to monitor and respond to user comments withautomated, optionally real-time responses using a commenting engine.Such responses may provide corrective information and may inhibit thefurther electronic distribution of low quality information, therebyreducing the computer, memory, and network resources that wouldotherwise be utilized in further distribution and maintenance of suchlow quality information.

For example, as will be described in greater detail, various onlineplatforms and electronic documents (e.g., social networking platforms,microblog platforms, content sharing platforms, webpages, etc.) may bemonitored for comments regarding a specified topic.

In response to determining that a comment has been made regarding thetopic, a determination may be made as to the intent or sentiment of thetopic (e.g., a negative comment, a positive comment, etc.). Based on thedetermined intent or sentiment of the comment, a determination may bemade as to whether a responding comment should be made.

If a determination is made that a responding comment should be made, adetermination may be made as to whether a standard, canned responseshould be used, or whether a response dynamically generated by acomputer system based on the detected comment should be used, or whetherthe detected comment should be transmitted to a human agent to manuallygenerated the response. The response may then be selected from a libraryof responses, generated by a response generation engine, or receivedfrom a human agent. The response may then be posted on the correspondingplatform, optionally in-line with the original detected comment.

Natural language processing may be utilized to determine the sentimentor intent of a comment. Optionally, in order to reduce the utilizationof computer resources (e.g., processor, memory, and/or networkresources), natural language processing is not performed on a given usercomment unless the user comment include a trigger phrase (including oneor more words, which may include slang word) in a defined triggerdictionary. If the user comment does include a trigger word or phrase,then natural language processing may be used to further analyze the usercomment to determine whether a response comment should be generated andif so, the content of the response comment.

For example, a trigger word may be a brand name of a product or service(e.g., a consumer product, a food product, a streaming service, ane-commerce service, a movie, a book, etc.), the name of a company ororganization, the name of a person (e.g., a celebrity, athlete,politician), the name of political entity (e.g., city, state, orcountry), phrases typically associated with negative sentiment (e.g.,junk, fake, greedy, total trash, hate it, etc.), phrases typicallyassociated with negative sentiment (e.g., awesome, amazing, best, love,etc.), and/or the like.

Optionally, the identification of the platform on which the comment wasposted may be utilized in determining the sentiment/intent of thecomment. For example, on certain platforms a given obscenity may bemeant as an insult, while on other platforms, the same obscenity may beinterpreted as a compliment. In addition, the number of like and/ordislike indications regarding the comment provided by other users may beutilized in in determining the sentiment/intent of the comment.

Certain examples will now be described with respect to the figures.

Referring to FIG. 1A, an example architectural environment isillustrated. A content monitoring and response system 104A may beconfigured to monitor content posted on one more networked locations(e.g., platforms/documents 106A-1 . . . 106A-N) and may optionally beconfigured to selectively provide automatic generation of responses tocontent (e.g., responses to comments comprising textual statements,still or video images, graphic content, animated content, audio content,etc.). The analysis of comments may optionally be performed usingnatural language processing for text and audio comments, and imagerecognition for visual comments.

The content monitoring and response system 104A may comprise a hostedcomputing environment that includes a collection of physical computingresources that may be remotely accessible and may be rapidly provisionedas needed (sometimes referred to as a “cloud” computing environment),thereby providing higher system uptime and reliability and a moreflexible and dynamic allocation of computer resources. The contentmonitoring and response system 104A may also include a data storedescribed in greater detail herein. The data store is optionally ahosted storage environment that includes a collection of physical datastorage devices that may be remotely accessible and may be rapidlyprovisioned as needed (sometimes referred to as “cloud” storage).

The content monitoring and response system 104A may be configured toenable certain authorized users (e.g., brand owners or someone acting ontheir behalf using systems 110A-1 . . . 110A-N) to specify platformsthat are to be monitored for comments or other user content and mayenables such authorized users to specify queries or triggers for usercontent. In addition, the content monitoring and response system 104Amay execute certain processes described herein, or portions thereof. Thecontent monitoring and response system 104A may provide authenticationand encryption services to provide for secure communication andrestricted access of data stores.

A plurality of disparate, distributed user systems 108A-1 . . . 108A-Nmay include standalone computers (e.g., desktop, laptop, tablet, smartphone, or other computer device), a centralized computer system, or acloud computing system. The user systems 108A-1 . . . 108A-N maycommunicate with various platforms 106A-1 . . . 106A-N (e.g., socialnetworking platforms, microblog platforms, video sharing platforms,product review sites, etc.). The platforms 106A-1 . . . 106A-N may beconfigured to receive user content, such as comments. A given platform106A may comprise a dedicated server system or may be cloud based.

For example, a given user system 108A and a given entity system 110A mayinclude some or all of the following: a display (e.g., a touch screendisplay), a microphone, a speaker, processing devices, memory, wirelessnetwork interfaces and/or wired network interfaces. The user systems108A-1 . . . 108A-N and entity systems 110A-1 . . . 110A-N may beconfigured to receive and render certain user interfaces via a browser,dedicated platform application, or otherwise. For example, the entitysystems 110A-1 . . . 110A-N may be configured to access and render userinterfaces described herein. The user systems 108A-1 . . . 108A-N may beconfigured to transmit content (e.g., user comments) to be posted on oneor more platforms. The entity systems 110A-1 . . . 110A-N may beconfigured to transmit content (e.g., promotional content, such as ads)to be posted on one or more platforms 106A-1 . . . 106A-N.

The communications between the entity systems 110A-1 . . . 110A-N andthe content monitoring and response system 104A may be respectivelyencrypted and decrypted by the entity systems 110A-1 . . . 110A-N andthe content monitoring and response system 104A.

The communications between the platforms 106A-1 . . . 106A-N and thecontent monitoring and response system 104A may be respectivelyencrypted and decrypted by the platforms 106A-1 . . . 106A-N and thecontent monitoring and response system 104A.

With reference to FIG. 1B, an example content monitoring and responsesystem 104A architecture is illustrated. A data store 100B is configuredto store trigger/query definitions 120B (e.g., specified by a brandowner), optionally in association with an identifier as to the source ofa given trigger/query definition (e.g., in the form of Boolean equationsand/or sentiment classifications). The data store 100B may store targetdata 121B, such as social account locators (e.g., URLs), page titles anddescriptions, email addresses, and/or other identifiers or information,that will be used to find content (e.g., user comments) to be examinedusing the trigger rules/queries.

The data store 100B may also store account records 122B of entities(e.g., brand owners) that may specify electronic and physical addresses(e.g., email address, messaging addresses, phone numbers, etc.), andinstructions as to how often and on what platforms an analysis of usercontent is to be performed using corresponding trigger/querydefinitions.

The data store 100B may also store historical data 124B of the resultsof queries and/or analysis of user content on various platforms.

The data store 100B may also store program code 126B that when executedby a processing unit 118B (e.g., one or more microprocessor devices) mayperform certain processes disclosed herein (including providing certainservices disclosed herein).

One or more wired and/or wireless network interfaces 116B may beprovided that enables the content monitoring and response system 104A tocommunicate with user devices, platform systems, and/or third partycontent detection and analysis systems.

A web service/app server 102B may provide webpages to devices (e.g.,associated with entities such as brand owners or an entity working onbehalf of a brand owner) that may include the example user interfacesdescribed herein. In addition or instead, the web service/app server102B may provide an application that may be downloaded to a system,where the application includes or provides access to user interfacesdescribed herein.

A comment discovery service 104B may be utilized to search/scrape one ormore destination platforms/documents for content that satisfies triggerrules/queries accessed from the data store 100B. For example, thecomment discovery service 104B may access social account locators (e.g.,URLs), page titles and descriptions, email addresses, and/or otheridentifiers or information from the data store 100B, and then perform acorresponding search for comments. By way of further example, thecomment discovery service 104B may obtain an access token (e.g., from anAPI explorer) and unique identifiers associated with target pages tothereby access content associated with the target pages.

A comment analysis service 106B may be utilized to analyze content(e.g., user comments) to understand the content and/or the sentiment ofthe content identified using the comment discovery service 104B and toclassify the content (e.g., hostile, sarcastic, threatening,complimentary, etc.).

For example, an artificial intelligence learning engine (such as a deepconvolutional neural network or recursive convolutional network) may beutilized to analyze and classify content. A trigger rule may be used todetermine if a response is to be provided based on the analysis. Forexample, a trigger rule may be in the form of a Boolean equationsincluding key phrases and/or in the form of sentiment classifiers, whereif the Boolean equation is satisfied for a comment and/or if the commenthas a certain sentiment classification (e.g., a negative classification)a response may be generated/selected and posted. For example, a responsegeneration service 108B may be utilized to dynamically generate commentsor select pre-generated a response to users comments using the resultsof the comment analysis service 106B.

As discussed above, optionally one or more predefined responses may beselected upon detecting a user comment that meets a correspondingtrigger. For example, if a comment is a positive comment, such as “Ilove [brand name]”, and the system classifies the comment as a positivecomment, the system may select a previously defined response forpositive response, such as “Thank you!”, “[brand name] loves you rightback!”

A notification service 1108 may be utilized to generate notificationsbased on the results of the comment analysis service 106B. For example,optionally, when a user comment satisfies a trigger and a responsecomment is generated and/or selected (where the response may include,text, audio, still images, video images, animations, icons, emoticons,and/or other content) an alert may be automatically generated andprovided to one or more notification destinations (e.g., destinationsystems, email addresses, short messaging system addresses, phonenumbers, etc.). The notification may include the generated/selectedresponse to the comment which caused the trigger to be satisfied, a linkto the platform where the comment was found, one or more commentsearlier in time in the same comment thread, a date/time when the commentwas posted, the trigger that was satisfied, and/or other data. Thenotification may include a screenshot of all or a portion of a webpageon which the comment was found.

A notification recipient may review the response and other data includedin the notification. A publish control may be provided which whenactivated by the recipient causes the response comment to be publishedon the platform on which the triggering comment was identified as aresponse comment to the triggering comment. A block control may beprovided which when activated causes the response comment to beinhibited from being posted. An edit control may be provided, which whenactivated, enables the recipient to edit the response comment (or entera completely new response) and to save the edit the response comment. Aselect comment control may be provided which when activated enables therecipient to select a different response comment from a set ofpredefined comments as a new response. The edited/selected responsecomment may be published by activating the publish control.

A report generation service 1128 may be utilized to generate reportsusing the results of the comment analysis service 106B. For example, areport may include quantifications of the number comments per sentimenttype per platform and overall for a specified period of time. Inaddition, the report may include a trigger cloud to provide visualrepresentation of content (e.g., single word, phrases including multiplewords, images, tags) where the importance and/or quantity of a givencontent item may be indicated using font size, color, and/or other formof emphasis. The generated report may include a trend graph showingchanges in relative sentiments over time.

One or more application programming interfaces 1148 may be utilized toaccess data from third party systems (e.g., sentiment data, overallsentiment metrics (e.g., percent positive sentiments, percent negativesentiments, percent neutral sentiments), like/dislike user data,mentions of a brand, keywords identified on platforms, etc.).

Thus, the content customization system 114A may provide authenticationand encryption services to provide for secure communication andrestricted access of content, such as the unique, custom content ofdistributors.

Certain example user interfaces will now be described with reference tothe figures.

As illustrated in FIG. 8A, an example user interface (e.g., provided bya dedicated application hosted on a user device or served by a webservice to a user device) may be provided via which an administratoruser (e.g., a user associated with a brand, product, service, company,famous person, or other topic) may input analysis triggers (e.g.,keywords, tags, names, phrases, images, videos) that are predicted topotentially be used by users of various platforms in their commentsabout the topic. The analysis may be associated with a project name(e.g., entered via a project name field or selected via a project namemenu).

Optionally, a user interface may be provided via which a user mayspecify one or more platforms (e.g., social networking platforms,microblog platforms, content sharing platforms, etc.) to be monitoredfor user comments. For example, a user may select one or more platformsfrom a menu of platforms and/or may specify one or more platforms viacorresponding fields.

As illustrated in FIG. 8B, an example user interface may be provided viawhich a user can set up an alert to be generated when a triggercondition is satisfied with respect to a user comment. The userinterface may include fields/menus via which a user can specify one ormore notification destinations (e.g., destination systems, emailaddresses, short messaging system addresses, phone numbers, etc.) towhich an alert notification is to be transmitted when a trigger issatisfied.

The alert notification may include the comment which caused the triggerto be satisfied, the trigger that was satisfied, an identification ofthe keywords and/or sentiment classification that satisfied the trigger,a link to the platform where the comment was found, one or more commentsearlier in time in the same comment thread, a date/time when the commentwas posted, and/or other data. The notification may include a screenshotof all or a portion of a webpage on which the comment was found. Theuser interface may include a name via which the user may enter a namefor the alert.

In order to learn what users are actually saying about a given topic,the content monitoring and response system 104A may enable a search andanalyze the pubic worldwide web or one or more platforms, using Booleansearch definition logic, Natural Language Processing and/or artificialintelligence. For example, the system may collect relevant posts,comments, and conversations, and/or discover what words, phrases, images(e.g., still, video, or animated images, with text (e.g., memes) orwithout text), that are being utilized by users to express how peoplefeel about the topic. Thus, for example, the system may monitor mentionsof a topic on one or more online platforms to identify user perceptionsregarding a topic and the number of mentions of the topic over aspecified period of time and/or on a given platform. By way ofillustration, a meme that is known to be very popular (e.g., asdetermined from a database of popular memes) may be monitored to detectusage of the meme (which may be a derogatory or complimentary meme) inconjunction with a given topic. Those findings of the foregoing searchand analysis may be used to specify new triggers and/or to modifyexisting triggers.

The system may also import and integrate social listening, artificialintelligence tools and/or findings from third party sentimentaggregators to further define what to look for and flag.

As illustrated in FIGS. 8C and 8D, example user interfaces may beprovided via which the user may specify a query including one or moreBoolean logic terms. For example, the user interface may enable the userto combine keywords or other content with operators (or modifiers) suchas logical operators (e.g., AND, NOT, OR, AND Not, and/or exclusive OR)to further produce more relevant results. For example, a Boolean searchcould be ([brand name] AND “hideous”). This would limit the searchresults to identify those comments containing at least the brand nameand the phrase “hideous”.

As illustrated in FIG. 8D, a test control may be provided and activatedby a user to cause a test of the trigger (sometimes referred to hereinas a query). Activation of the test control may cause one or moreplatforms to be examined and a determination may be made as to comments(or phrases within comments) that satisfy the Boolean equation. Theresults may indicate graphically and/or numerically, the absolute and/orrelative quantity of phrases that satisfy the Boolean equation. Forexample, a trigger cloud may be used to provide a visual representationof content (e.g., single word, phrases including multiple words, images,tags) where the importance and/or quantity of a given content item maybe indicated using font size, color, and/or other form of emphasis. Thetrigger cloud may enable a viewer to quickly perceive the mostemphasized content items, and use such information to modify the trigger(e.g., by adding or deleting terms from the Boolean equation) toidentify more relevant user comments.

As illustrated in FIG. 8E, an example dashboard user interfacecomprising fields and controls enables a project to be defined, a nameassigned to the project, locators (e.g., URLs) associated with platformsto be monitored entered, and the source of response comments specified.(e.g., a response module associated with the system providing the userinterfaces or a third party response generator system). Links may beprovided to other user interfaces, alerts, tools, and data discussedelsewhere herein.

Optionally, an administrator user interface may include a control viawhich an administrator user may specify that automatically generatedand/or selected responses are to be automatically posted in response toa triggering comment on the corresponding platform without providing thecomment for review to a human and/or without requiring approval from ahuman prior to posting.

FIG. 2 illustrates an example implementation of a natural languageprocessing engine which may be utilized by the comment analysis service106B. A lexical analysis module 202 divides the text into paragraphs,sentences, and words. The lexical analysis module 202 may detectpunctuation, such as commas, periods, exclamation points, colon,semicolons, carriage returns, brackets, and/or parenthesis indetermining how to divide the text into paragraphs, sentences, andwords.

A syntax analysis module 204 may parse the text to determine the meaningof the user comment. The syntax analysis module 204 may generate astructural representation of the text input after checking for correctsyntax as per formal grammar rules. For example, a data structure may begenerated in the form of a generally in the form of a (e.g., a parsetree or abstract syntax tree).

A semantic analysis module 206 analyzes the real meaning from the text,may discover the meaning of colloquial speech in online posts, mayextract relevant and useful information from large bodies ofunstructured data, and/or may uncover specific meanings of words used inforeign languages mixed with a local language (e.g., English). LatentSemantic Analysis (LSA) may be used to extract and represent thecontextual-usage meaning of words by statistical computations applied toa large amounts of text. LSA may analyze and identify patterns inunstructured collection of text and the relationship between them.

Optionally, sentiment analysis may be performed on text to understandthe opinion expressed by the text. For example, the sentiment may bequantified with a polarity (a positive or negative value). The overallsentiment may be inferred as positive, neutral or negative from the signof the polarity score. Optionally, a given comment may be assigned asentiment score (e.g., 1-5, where 1 and 2 are classified as a negativesentiment, 4 and 5 are classified as a positive sentiment, and 3 isclassified as a neutral sentiment).

The semantic analysis module 206 may assign text elements respectivelogical and grammatical roles. The semantic analysis module 206 mayanalyze context in the surrounding text and the text structure todisambiguate the proper meaning of words that have more than onedefinition. The semantic analysis module 206 may analyze the logicalstructure of a given phrase, clause, sentence, or paragraph to identifythe most relevant elements in the text and identify the topic discussed.The semantic analysis module 206 may also understand the relationshipsbetween different concepts in the text and use such understanding tounderstand the subject of the text.

A discourse analysis module 208 analyzes the text to determine thesemantic conveyed by the text language and may identify the discourserelationships between clauses, sentences, and/or paragraphs to ensurecoherence (e.g., where the meaning of a sentence may depend upon themeaning of the immediately preceding sentence). For example, a givensentence may provide elaboration or a contrast with a precedingsentience. The discourse analysis module 208 may also analyze text toidentify a text act, such as a question, assertion, etc. The discourseanalysis module 208 may split the text into discourse units, ensure theattachment between discourse units, and then label links betweendiscourse units with discourse relations. The discourse analysis modulemay identify the topic structure, the coherence structure, thecoreference structure, and the conversation structure for conversationaldiscourse.

A pragmatic analysis module 210 analyzes the text and may reinterpretwhat was said to determine what was actually meant. For example, thepragmatic analysis module 210 may understand how units of speech (e.g.,sentences) are used in different situations and how use affects theinterpretation of the sentence. Thus, the pragmatic analysis module 210may determine the likely intention of the speaker and the conversationto aid in the interpretation of the unit of speech.

Optionally, Bidirectional Encoder Representations from Transformers(BERT) may be used to transform comments to word embeddings. Theembeddings, may be used to train a Convolutional Neural Network (CNN)using to identify certain sentiments (e.g., hate, approval, dislike,etc.). The CNN may be trained using a dataset of comments and differenttypes of sentiments (love, like, hate, ridicule, obscenities, insults,etc.). The CNN may include an input layer, one or more hidden layerscomprising neurons connected by weights, the weights corresponding tothe strength of the connection between neurons, and an output layer.Each time a neural layer is trained on a sample comment, the differencebetween the predicted and true output causes an update in the weights abackpropagation process.

For example, the CNN may be used to tag a comment using one or moresentiment types. The tags assigned by the CNN may be compared to“correct” tags previously assigned to the same comments, and if the CNNincorrectly tag a comment, and error function may be used to generateweight updates.

Optionally, a recursive neural network or a recursive convolutionalneural network may be used to perform sentiment classification.

Once a trigger word has been detected in a comment and the commentanalyzed to determine the comment intent/sentiment, a determination maybe made as to whether or not a comment should be generated in response.

FIG. 3A illustrates an example supervised learning model trainable toclassify the sentiment of user comments. Training text 302 is providedto the supervised learning model to enable the supervised learning modelto learn and predict categories/classes for the input text. One or morefeature vectors 304 may be created using information describingcharacteristics of the input text.

The supervised learning model may use labels corresponding to predefinedcategories/classes that the model will predict (e.g., a sentiment scoreof 1-5, where 1 and 2 are classified as a negative sentiment, 4 and 5are classified as a positive sentiment, and 3 is classified as a neutralsentiment). A machine learning model 306 utilizes an algorithm throughwhich the model is able to handle text classification. A predictivemodel 314 is trained on the historical dataset to enable the predictivemodel 314 to perform label predictions.

Once trained, user comments 310A may be used as inputs, and one or morecorresponding feature vectors 312 may be generated using informationextracted from the user comments 310A. Optionally, to reduce theutilization of processing resources and memory utilization, certain textthat may not be relevant to determining sentiment may be filtered out.For example, language stopwords (e.g., “etc.”, “the”, “is”, “in”, “for”,“where”, “when”, “to”, “at”, or the like), URLs or links, and/orindustry specific or highly technical words may be filtered out.Optionally, to further reduce the utilization of processing and memoryresources, stemming (the stripping off of suffixes (“ing”, “ly”, “es”,“s”, etc.)) and/or lemmatization (obtaining the root form of the word)may be performed prior to or after identifying and removing stopwords.Optionally, the comments may be truncated to a first length and/orpadded (e.g., with zeros) so as to be the first length so that allcomments will have the same length for further processing. Optionally,comment words may be converted to a corresponding integer value. Thepredictive model 314 may then classify the sentiment of the usercomments 310A using corresponding sentiment labels 316.

FIG. 3B illustrates an example neural network which may be used toclassify comments. The neural network may include an input layer 302B,one or more hidden layers 304B, and an output layer 306B. Optionally,the neural network may include (e.g., as the first hidden layer) a KerasEmbedding Layer (which turns positive integers (indexes) into densevectors of fixed size). The neural network may be configured as a feedforward network. The neural network may be configured with ashared-weights architecture and with translation invariancecharacteristics. One or more hidden layers may be configured asconvolutional layers (comprising neurons/nodes connected by weights, theweights corresponding to the strength of the connection betweenneurons), pooling layers, fully connected layers and/or normalizationlayers. The neural network may be configured with pooling layers thatcombine outputs of neuron clusters at one layer into a single neuron inthe next layer. Max pooling and/or average pooling may be utilized. Maxpooling may utilize the maximum value from each of a cluster of neuronsat the prior layer. Back propagation may be utilized, and thecorresponding neural network weights may be adjusted to minimize orreduce the error. Optionally, the loss function may comprise the BinaryCross Entropy loss function.

FIG. 4 illustrates an example process of analyzing content, determiningif a response is to be selected or generated, and if so, posting aresponse. At block 402, target data used to identify platforms andonline documents may be accessed (e.g., from a local or remote datastore). For example, the target data may include social account locators(e.g., URLs), page titles and descriptions, email addresses, accesstokens, and/or other identifiers or information. At block 404, triggerrules may be accessed (e.g., from a local or remote data store). Forexample, the trigger rules may include Boolean equations, sentimentclassifications/scores and/or other rules that may be used to determinewhether a response to a content (e.g., a comment) located on a targetplatform or document is to be provided. By way of example, the targetdata and/or the trigger rules may have been specified in whole or inpart by a brand owner.

At block 406, using the accessed target data, a given platform/onlinedocument may be accessed, and user comments on the platform/onlinedocument may be identified and accessed. At block 408, a given commentmay be analyzed (e.g., using one or more of the techniques describedherein). For example, a comment may be analyzed to identify negativeemoticons, negative video memes, key phrases (which may include one ormore words), and/or comment sentiment (e.g., using a recursiveconvolutional neural network, a recursive neural network, and/or aconvolutional neural network). Optionally, feedback by other users ofthe platform/document regarding the comment (e.g., the number of likeand/or dislike indications regarding the comment, the number of userresponses to the comments, the number of times the comment was shared,and/or the like) may be used in determining the comment sentiment.

At block 410, using a the accessed trigger rules, a determination may bemade as to whether to respond to the comment. For example, if theycomment included certain phrases and/or had a certain sentimentclassification (e.g., a score of 1-2 or 4-5 on a scale of 1-5, where 1and 2 correspond to negative sentiment, 4 and 5 correspond to a positivesentiment, and 3 corresponds to a neutral sentiment), a determinationmay be made that a response should be provided to the comment. If adetermination is made that a response is not to be provided (e.g.,because the comment did not include phrases that satisfied the triggerrule and/or did not have a sentiment classification that satisfied thetrigger rule), the next comment may be analyzed.

If a determination is made that a response is to be provided, at block412, a response may be selected or generated. For example, withreference to FIG. 5, a matrix may be defined for a given project thatreferences responses to sentiment labels and key phrases. In thisexample, there is no responses in the matrix if there is a neutralsentiment label (e.g., a label of 3), but there are responses for bothpositive sentiments with certain key phrases (e.g., in response tocomment that had the key phrase “Love it” with a sentiment label of 5, aresponse of “Thank you, we aim to please” may be selected) and negativesentiments with certain key phrases (e.g., in response to comment thathad the key phrase “adulterated ingredients” with a sentiment label of1, a response of “We are product that all our ingredients are certifiedorganic” may be selected).

Optionally, instead, the comment may be transmitted to an administratoror other user to review and to manually enter a response for posting.Optionally, the comment may be transmitted to an administrator or otheruser with one or more selected responses (and optionally with theidentified keywords visually emphasized and/or with the sentimentclassification) so that the comment may be reviewed and the proposedresponse(s) may be selected, edited, and/or replaced by the user forposting

At block 414 the response may be posted to the correspondingplatform/online document.

Further, conventionally there are numerous online social networking andcommunities that provide users with an interface for interacting withother users. In some cases, online social networking is combined withoffline elements such as face-to-face events. The ability of users topurchase products and obtain information from online services isrevolutionizing the way business is done.

In addition, although existing social networks are powerful tools forinteracting with people who have similar interests, such communities arenot organized for users who have artistic talent (e.g., musical, acting,speaking, and/or other talents) to receive a part of resources, such asbenefits/proceeds (e.g., revenue) generated derived from byproducts(e.g., online distribution of the user's content) of their artistictalent and popularity.

Aspects of the disclosure relate to systems and methods for providingone or more services to an artist or a group of artists (who may bereferred to as “content generator(s)”) meeting a certain threshold offeedback and apportioning resources, such as benefits/proceeds (e.g.,revenue), derived from services associated with content generator'sperformance, and distribution of third party content (e.g.,advertisements) and content generator's media content. Information abouta content generator and performance data associated with the contentgenerator may be obtained. In certain embodiments, the performance datais a media file (e.g., audio, video, and/or text or any combinationthereof). Once obtained, this performance data is then exposed to thecommunity for feedback in a manner that is determinative of the outcomeassociated with a particular content generator. The community mayinclude peers and other content generators that generally have aninterest in the content generator's activities (e.g., a genre of music,videos, shows, podcasts, etc. Associated with the content generator'scontent) but may also comprise users who enjoy participating in anonline community where new content generator content is plentiful.

After meeting a certain threshold of feedback from the online community,a decision is made to extend an offer to the content generator. Such anoffer may include one or more services to promote the content generator,distribute the content generator's performance data, and allow forproceeds participation associated with such service.

Another aspect relates to the administration and monetization of methodsof use of the submitted performance data and/or content generator'sperformance activity. In certain embodiments, the submitted performancedata may be licensed for use by third parties for distribution throughoffline channels such as cell phones, podcasts, cable television,satellite television, and/or broadcast television. The submittedperformance data may be licensed for reproduction on DVDs, videotapesand/or other formats for sale by the licensee. In embodiments, thecontent generator would be able to opt-in to any licensing program.

Various types of content generator activities benefit from the use ofsystems and methods disclosed herein. As such, content generatoractivities are not limited solely to the performing arts but should beviewed as any creative activity that gives rise to a user base when madeavailable online. In addition to music, video and other performancesworks of authorship, such as blogs or other online commentary, may beconsidered a content generator activity.

The online community may optionally be utilized in determining whatcontent generator activities should be further produced and enables theproducing content generator(s) to share in the advertising and otherpossible proceeds streams once approval has been given. For example, theonline community may control a performance's rise in popularity and mayenable one or more sources of proceeds generated from the outcomeassociated with a particular content generator.

When a decision is made to select a content generator (e.g., based onthe online community interaction with the content generator and/orcontent generator content), one or more services and associated proceedsparticipation may be extended to the content generator. Examples of suchservices may include, but are not limited to, advertisement placement,marketing, sponsorship, touring, licensing, extensions to contentgenerator website/page, and/or distribution of content generator'sperformance data. Some aspects of the service may include either offlineand/or online elements.

The content generator's submitted performance data may then beoptionally offered for free or for sale. For example, sales occur insuch cases where the system for obtaining media data or performance datais separate from a purchasing system. Submitted data can be synchronizedacross a network with the purchasing system so that it becomesaccessible for sale. The content generator receives a portion ofproceeds generated through one or more services and the portion may be afixed percentage or a percentage based on a sliding scale that adjustsautomatically to the proceeds generated from the services related to thecontent generator performance data. This sliding scale is unique toadvertising proceeds participation and is an indirect measure of thecontent generator's popularity. This enables a content generator toreceive proceeds in free download environments and provides additionalproceeds to the content generator in pay-for-download venues.

Embodiments in which submitted performance data meeting a certainthreshold of feedback and then provided through a service might beviewed as providing a rich example of the variety of possibilities foroptimal proceeds participation and for the ability to relate suchproceeds directly to the service. Users might pay a fee for the service,pay on a subscription basis, pay per show, pay per download, or pay onsome other basis. Advertisers, sponsors, or programmers might pay forsuch service and allow users, for example, to view submitted performancedata for free. In other embodiments users are given everything but musicand video downloads for free. Optionally proceeds may be shared withusers who generate business or traffic to the system by referring usersand getting more and more referred business from content generators andusers.

This party content providers (e.g., advertisers) find the ability tocouple the third party content provider's content (e.g., anadvertisement) with the content generator's performance data to be veryvaluable, and might be expected to pay for such services based oncriteria such as number of ad viewings for which viewing is possible(e.g., in a manner analogous to Web ad impressions, pay-per-impression),the number of times an advertisement a link is activated (e.g., in amanner analogous to click-throughs, pay-per-click), the number ofactivations (such as if multiple activation opportunities per ad areprovided, and also analogous to click-throughs), the number of leadsobtained (pay-per-lead), the number of transactions completed(pay-per-transaction), or other such variations. Many of these andsimilar pricing schemes might be applicable to embodiments providingoffline service as well, such as including advertisement in touring orlive performance venue.

A proportionate share of proceeds for example can be provided to acontent generator selected by the community for performances conductedoffline. Hence television shows, plays, and any other type ofperformances that occur offline may still trigger a proportionatepayment to the content generator if the performing content generator wasinitially chosen by the online community or viewers whose votestriggered the content generator to be signed with a representative suchas a record label or other entity promoting the content generator'swork.

Optionally, having been chosen by the community itself is notdeterminative of being able to obtain a proportionate share of proceeds.A content generator that becomes increasingly popular in the communityas judged by page views, number of friends, fans or some other measureof popularity may obtain the same status as one originally elected bythe community once a certain threshold is reached. A content generatoror user, for example, that has 1 million page views may qualify for aproportionate share of proceeds on advertising, items sold and promotedon their personal profile page.

Submitted performance data can be optionally made available fordistribution on a service as a ring tone data or a general media filethat can be downloaded into a cell phone, media playable device, orother computation device where use of such files might be applicable. Inthe instance where the performance data is video, a user may downloadthe video data for purchase via online distribution channels or bebought on various media through a third party distribution network.

By apportioning proceeds with a content generator, the content generatorhas an incentive to provide performance data and refer users and othercontent generators to the system. This helps the system build a networkincluding an unlimited number of advertisers, getting more and morereferred business from content generators and users. At the same time,the system can reduce its own advertising ventures and expenses.Furthermore, through the use of user account, the system can apportionproceeds with content generator and end users, and for each of itsvarious online sites that sell intangible and tangible items.

Optionally, systems and methods may be provided that enable themonitoring of recorded or live performances (or other content) ofcertain users and viewer interaction with such content (e.g.,distributed electronically such as via online social media platforms).Such systems and methods may be configured to select content generatingusers meeting a certain threshold of feedback and apportioning benefitderived from services, optionally based on network traffic (e.g., views,playbacks in whole or in part, sharing, and or the user of usergenerated data) associated with a content generating user's performancedata, and distribution of advertisement and content generating user'smedia content.

As aspect of the disclosure relates to systems and methods that may beutilized to enable users to benefit from (e.g., monetize) their content.An aspect of the present disclosure relates to systems and methods thatoptionally enables independent and other content generating users toexploit their content and have a community of content consumers/endusers democratically determine what content they want to see more of byproviding feedback regarding such content.

As aspect of the disclosure relates to systems and methods configured toprovide one or more services to content generating user meeting one ormore feedback thresholds and apportion benefit derived from the servicesrelated to content generating user's performance activities anddistribution of performance data.

The general methodology for obtaining and synchronizing performance datainvolves obtaining information about a content generating user andobtaining performance data associated with the content generating user.The term performance data refers to any content (e.g., video content(which may include an audio track), audio-only content, text content,etc.) captured within a tangible medium such as computer memory. Suchperformances data may be recorded in audio and/or video form prior to orsimultaneous with submission to the community. Although certain examplesherein may relate to musical performances, the systems and methodsdisclosed herein may be used for other types of performances (e.g.,acting performances, comedy performance, dancing performances, readings,unboxings, interviews, video performances or works of art such asdrawings, paintings or other visual renditions made by a contentgenerating user).

Information related to the content generating user may be obtained inany manner including via telephonic communication, an application hostedon a user device (e.g., mobile smartphone, laptop, tablet, desktopcomputer, video streamer, set top box, smart television, etc.), and/orwebsite based communication. Likewise, by way of example, media such asperformance data may be obtained over a telecommunication medium such asa wireless or wire based telephone channel, over an Internet basedstreaming or telephone channel, as an upload to a cloud based storagesystem, or otherwise.

For example, online personal profile pages hosted by a contentdistribution system enables users to use a VoIP (Voice over InternetProtocol) communication link (e.g., Skype™) to talk with friends andother users (via voice only or via a video/voice call), and stream theircontent playlist and content across the network link (e.g., the Internetor other internetwork or intra-network) to the correspondingdestination. When a content generating user's performance is captured invideo form (e.g., 3GPP, AVI, FLV, MOV, MPEG4, MPEGPS, WebM, WMV or othervideo encoding format) that video and the accompanying audio data may beuploaded over the network to the content distribution system to enable acommunity viewers interact with the video and for determination as tothe popularity of the video/performance data.

In addition, systems and methods described herein enable a user tocreate their own custom radio station on their personal profile pagewith their own playlist/content they may web cast from their personalpage. The custom station may be provided and/or accessed using suchchannels as RSS, podcasting, email, text messaging, via a search engine,a podcast app, or otherwise. For example, a search user interface thatenables a user to search for content by content name, metadata,performer name, and/or otherwise. A search engine may receive the searchquery locate matches and generate search results which may be displayedto the user. The user may select one or more of the search results andselect a radio station. The user may add the selected search results tothe selected radio station. Optionally, a user interface may be providedthat enables the user to create a new radio station and name the radiostation.

Certain services made be available to content generating users based onviewer feedback. Such services may include, by way of example, one orany combination of media production, marketing, content generating usermanagement, sponsorship, media or performance data distribution,touring, and licensing. A service may be an online service and provisionof the service may be automated by a server process. For example, suchserver-provided service may be applied to online distribution ofsubmitted performance data. Other services may utilize an externalintermediary (e.g., a business manager or agent) who provides theservice initiated by a server process using a technologicalcommunication channel (e.g. email, user interface notification). Whereonline interfacing is applicable, presenting aspects of service to auser can be implemented using any graphical user interface or webinterface configured to obtain data from a server and present the data.Optionally, any form of monetization arising from presenting third partycontent, such a advertisements (e.g., displayed in conjunction withperformance data) during the service provided to content generating usermay be shared with the content generating user.

FIG. 6 illustrates an example flow diagram of a process configured toprovide one or more services to content generating users meeting one ormore thresholds of feedback. Further, the process may be utilized toapportion benefits derived from the services related to contentgenerating user's performance activities and distribution of submittedperformance data. Processing may be performed using a server orcloud-based system coupled to a data repository and to a globalcommunication network. At step 602, the process begins.

For a performance data exposed to the community for feedback (e.g., viaa website, an app, podcast, or otherwise), viewer feedback may bemonitored and analyzed. Such feedback may include one or more of views,number of likes, positive or negative textual or icon comments, numberof friends, fans, and/or some other measure of popularity. At step 604,the content generating user is selected on reaching a predeterminedthreshold condition or value of feedback generated from communityrankings and feedback mechanisms. Any manner of determining a thresholdcondition or value for selecting content generating user includingrelative or fixed number of feedback, or qualitative or quantitativefeedback condition may be used.

For example, but not limited to, the selection of a content generatinguser may occur upon a feedback reaches a threshold condition or value(e.g., when the ranking of submitted performance on a genre within themusic category reaches Top 10 or other level; or when the positivefeedback on submitted performance data for a sub-category within thevideo category exceeds 32% (or other percentage) of the quantity of theregistered users; or when the content generating user is voted as awinner of an audition, etc.). Selection of a content generating user maytake place periodically (e.g., weekly, or quarterly).

Resource offers, such as a benefit participation and a service offer,are presented to the content generating user at 606. Such offers may bepresented to a user using a graphical user interface or web interfaceconfigured to obtain data from a server and present the data to aplurality of users. For example, a Skype™ or other such communicationinterface may provide a free interface between users and contentgenerating user. The presentation of offers allows a selected contentgenerating user to opt into one or more offered services. An offer maybe dynamically generated prior to presentation depending on availabilityof one or more service and whether the service has a different thresholdvalue or condition.

For example, an additional service may be available for contentgenerating users reaching a second, relatively higher threshold value orcondition. Services may also be offered depending on a condition, whichis recorded in a database, as set up by advertisers, sponsors, orprogrammers seeking a certain target audience. The selected contentgenerating user is offered compensation from the benefit generated fromthird party content items displayed in association with content of thecontent generating user or affiliate page and/or sale of any media withthe submitted performance data. Third party content items may be in anyform and include but are by no means limited to online third partycontent items (e.g., video, image and other types of ads, etc.). Uponaccepting an offer, the content generating user's user account isconfigured to be associated with a benefit participation process.

After presentation, submitted performance data is associated with anthird party content item (e.g., advertisement) process at 608. The thirdparty content item process controls and tracks the instances or scoresfor viewing of third party content items (e.g., advertisements)retrieved from a data repository and displayed with the media contentsof the content generating user or affiliate page on the system. Thethird party content item process is coupled with the benefitparticipation process, which records the instances or scores of viewingthird party content item on the user account of selected contentgenerating user. By way of example, popularity may be determined byvotes rather than a ranking, although both techniques may be utilized.

For advertisement on an offline service, advertisement media may becoupled with performance data or be displayed at a venue for a contentgenerating user's performance, such as a banner at the concert during acontent generating user concert tour or previewing advertisement videoon media containing video of the submitted performance data. Keeping inthe spirit of the disclosure, to enable the benefit participation, anyprocess that allows tracking of instances of third party content itemssuch as number of ad viewings for which viewing is possible, number forwhich third party content item link is activated, number of activations,number of leads obtained, number of transactions completed, or othersuch variations.

Distribution of submitted performance data occurs at one or anycombination of: providing online distribution, providing to third partyfor distribution, and providing media sales at 610A, 610B, 610Crespectively. Submitted performance data may be synchronized with apurchasing system so that it becomes accessible. In another embodiment,submitted performance data may be available for free on onlinedistribution coupled with third party content item placement which inturn generates benefit using methods discussed herein. In theseinstances where the performance data is video, user may upload the videodata for purchase via the Internet or be bought on various media througha third party distribution network. In these embodiments, the submittedperformance data may be licensed for use by third parties fordistribution through offline channels such as cell phones, podcasts,cable television, satellite television, and/or broadcast television. Thelicensee may license the submitted performance data for reproduction onDVDs, videotapes and/or other formats for sale.

A process to provide one or more services and associated benefitparticipation is enabled at 612. A system may execute one or moreprocesses to provide service and benefit participation as optioned by aselected content generating user at 606. The benefit participationprocess tracks one or more benefit-generating activities (e.g.advertisement, distribution of the content generating user's performancedata, licensing, sponsorship, and touring) and apportions a percentageof benefit to the selected content generating user's user account.

In certain embodiments, the service process may route email or establishtelephone communication between a service provider intermediary and acontent generating user thus initiating the service, which is thenprovided to the content generating user by the service providerintermediary. For example, the process may notify a business manager byemail via a global communication network to secure sponsorship for aselected content generating user. Such service provided by a serviceprovider includes media production, marketing, content generating usermanagement, sponsorship, trading or distribution, touring, andlicensing.

FIG. 7 is a flow diagram that illustrates an embodiment ofbenefit/resource distribution participation process. Processing startsat 702. Resource/benefit participation and a service offer are presentedat 704. An offer can be presented to a user using any graphical userinterface or web interface configured to obtain data from a server andpresent the data to a plurality of users. The presentation of offer 704allows a selected content generating user to opt into one or moreservice. An offer may be dynamically generated during the presentationdepending on availability of one or more service and whether the offerof a service has a different threshold value or condition (e.g. sponsorlooking for a winner of a music audition of a specific genre).

Upon accepting an offer, the user account of the content generating useris configured at 706 to allow a database to record on the user accountand track scores or instances of one or a combination ofbenefit-generating sub processes such as online viewing of third partycontent (e.g., advertisements) on the user's media content (e.g.,webpage) 708A, digital media download of the content generating user'sperformance data 708B, and/or online referrals initiated by the selectedcontent generating user 708C.

Determination of a percentage for apportioning benefit takes placeperiodically at 710. The percentage determination may be based on asliding scale in which higher percentage of benefit is apportioned tothe user account for a higher number of third party content viewings708A, digital downloads 708B, and referrals 708C. The determination of apercentage may give a greater weight to one or more scores tracked from,for example, third party content viewing 708A and digital media downloadof the content generating user's performance data 708B. The process mayend at 712.

An aspect of the disclosure relates to a non-transitorycomputer-readable medium comprising computer-readable instructions whichwhen executed by one or more processors cause said one or moreprocessors to perform a process comprising: obtaining a media filecomprising a content item associated with a content source; storing saidmedia file in a tangible, non-transitory computer-readable mediumaccessible by one or more computers over a network; associating saidmedia file with an account associated with said content source;presenting said media file to a community including one or more viewers;obtaining feedback on said content item from at least one viewer;determining that a threshold or condition, based at least in part onsaid feedback, has been met; enabling said content source to participatein a first program based at least on said threshold or condition beingmet; enabling advertisement content to be presented on devices of one ormore members of said community in conjunction with said media file afterenabling said content source to participate in the first program; afterenabling advertisement content to be presented on devices of one or moremembers of said community in conjunction with said media file, based inpart on at least one activity comprising subsequent communityinteraction with said media file: providing an incentive to said contentsource.

An aspect of the disclosure relates to a method, comprising: withrespect to a media submitter, determining whether a threshold orcondition has been met; enabling said media submitter to participate ina first program based at least on said threshold or condition being met;obtaining a media file from the media submitter; storing said media filein a tangible, non-transitory computer-readable medium accessible by oneor more computers over a network; associating said media file with anaccount associated with said media submitter; presenting said media fileto a community including one or more viewers; obtaining feedback on saidmedia file from at least one viewer; enabling advertisement content tobe presented on devices of one or more members of said community inconjunction with said media file after enabling said media submitter toparticipate in the first program; after enabling advertisement contentto be presented on devices of one or more members of said community inconjunction with said media file, based in part on at least one activitycomprising subsequent community interaction with said media file:providing an incentive to said media submitter.

An aspect of the disclosure relates to a system, comprising: a mediafile data repository accessible by one or more computers over a network;a server configured to: with respect to a media submitter, determiningwhether a threshold or condition has been met; enabling said mediasubmitter to participate in a first program based at least on saidthreshold or condition being met; obtain a media file from said mediasubmitter; store said media file in the media file data repository;associate said media file with an account associated with said mediasubmitter; present said media file to a community including one or moreviewers; enable said media submitter to participate in a first programbased at least on said threshold or condition being met; enableadvertisement content to be presented on devices of one or more membersof said community in conjunction with said media file after enablingsaid media submitter to participate in the first program; after enablingadvertisement content to be presented on devices of one or more membersof said community in conjunction with said media file, based in part onat least one activity comprising subsequent community interaction withsaid media file: provide an incentive to said media submitter.

The methods and processes described herein may have fewer or additionalsteps or states and the steps or states may be performed in a differentorder. Not all steps or states need to be reached. The methods andprocesses described herein may be embodied in, and fully or partiallyautomated via, software code modules executed by one or more generalpurpose computers. The code modules may be stored in any type ofcomputer-readable medium or other computer storage device. Some or allof the methods may alternatively be embodied in whole or in part inspecialized computer hardware. The systems described herein mayoptionally include displays, user input devices (e.g., touchscreen,keyboard, mouse, voice recognition, etc.), network interfaces, etc.

The results of the disclosed methods may be stored in any type ofcomputer data repository, such as relational databases and flat filesystems that use volatile and/or non-volatile memory (e.g., magneticdisk storage, optical storage, EEPROM and/or solid state RAM).

The various illustrative logical blocks, modules, routines, andalgorithm steps described in connection with the embodiments disclosedherein can be implemented as electronic hardware, computer software, orcombinations of both. To clearly illustrate this interchangeability ofhardware and software, various illustrative components, blocks, modules,and steps have been described above generally in terms of theirfunctionality. Whether such functionality is implemented as hardware orsoftware depends upon the particular application and design constraintsimposed on the overall system. The described functionality can beimplemented in varying ways for each particular application, but suchimplementation decisions should not be interpreted as causing adeparture from the scope of the disclosure.

Moreover, the various illustrative logical blocks and modules describedin connection with the embodiments disclosed herein can be implementedor performed by a machine, such as a processor device, a digital signalprocessor (DSP), an application specific integrated circuit (ASIC), afield programmable gate array (FPGA) or other programmable logic device,discrete gate or transistor logic, discrete hardware components, or anycombination thereof designed to perform the functions described herein.A processor device can be a microprocessor, but in the alternative, theprocessor device can be a controller, microcontroller, or state machine,combinations of the same, or the like. A processor device can includeelectrical circuitry configured to process computer-executableinstructions. In another embodiment, a processor device includes an FPGAor other programmable device that performs logic operations withoutprocessing computer-executable instructions. A processor device can alsobe implemented as a combination of computing devices, e.g., acombination of a DSP and a microprocessor, a plurality ofmicroprocessors, one or more microprocessors in conjunction with a DSPcore, or any other such configuration. Although described hereinprimarily with respect to digital technology, a processor device mayalso include primarily analog components. For example, some or all ofthe rendering techniques described herein may be implemented in analogcircuitry or mixed analog and digital circuitry. A computing environmentcan include any type of computer system, including, but not limited to,a computer system based on a microprocessor, a mainframe computer, adigital signal processor, a portable computing device, a devicecontroller, or a computational engine within an appliance, to name afew.

The elements of a method, process, routine, or algorithm described inconnection with the embodiments disclosed herein can be embodieddirectly in hardware, in a software module executed by a processordevice, or in a combination of the two. A software module can reside inRAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory,registers, hard disk, a removable disk, a CD-ROM, or any other form of anon-transitory computer-readable storage medium. An exemplary storagemedium can be coupled to the processor device such that the processordevice can read information from, and write information to, the storagemedium. In the alternative, the storage medium can be integer to theprocessor device. The processor device and the storage medium can residein an ASIC. The ASIC can reside in a user terminal. In the alternative,the processor device and the storage medium can reside as discretecomponents in a user terminal.

Conditional language used herein, such as, among others, “can,” “may,”“might,” “may,” “e.g.,” and the like, unless specifically statedotherwise, or otherwise understood within the context as used, isgenerally intended to convey that certain embodiments include, whileother embodiments do not include, certain features, elements and/orsteps. Thus, such conditional language is not generally intended toimply that features, elements and/or steps are in any way required forone or more embodiments or that one or more embodiments necessarilyinclude logic for deciding, with or without other input or prompting,whether these features, elements and/or steps are included or are to beperformed in any particular embodiment. The terms “comprising,”“including,” “having,” and the like are synonymous and are usedinclusively, in an open-ended fashion, and do not exclude additionalelements, features, acts, operations, and so forth. Also, the term “or”is used in its inclusive sense (and not in its exclusive sense) so thatwhen used, for example, to connect a list of elements, the term “or”means one, some, or all of the elements in the list.

Disjunctive language such as the phrase “at least one of X, Y, Z,”unless specifically stated otherwise, is otherwise understood with thecontext as used in general to present that an item, term, etc., may beeither X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z).Thus, such disjunctive language is not generally intended to, and shouldnot, imply that certain embodiments require at least one of X, at leastone of Y, or at least one of Z to each be present.

While the phrase “click” may be used with respect to a user selecting acontrol, menu selection, or the like, other user inputs may be used,such as voice commands, text entry, gestures, etc. For example, a clickmay be in the form of a user touch (via finger or stylus) on a touchscreen, or in the form of a user moving a cursor (using a mouse ofkeyboard navigation keys) to a displayed object and activating aphysical control (e.g., a mouse button or keyboard key). User inputsmay, by way of example, be provided via an interface or in response to aprompt (e.g., a voice or text prompt). By way of example an interfacemay include text fields, wherein a user provides input by entering textinto the field. By way of further example, a user input may be receivedvia a menu selection (e.g., a drop down menu, a list or otherarrangement via which the user can check via a check box or otherwisemake a selection or selections, a group of individually selectableicons, a menu selection made via an interactive voice response system,etc.). When the user provides an input or activates a control, acorresponding computing system may perform a corresponding operation(e.g., store the user input, process the user input, provide a responseto the user input, etc.). Some or all of the data, inputs andinstructions provided by a user may optionally be stored in a systemdata store (e.g., a database), from which the system may access andretrieve such data, inputs, and instructions. The notifications and userinterfaces described herein may be provided via a Web page, a dedicatedor non-dedicated phone application, computer application, a shortmessaging service message (e.g., SMS, MMS, etc.), instant messaging,email, push notification, audibly, and/or otherwise.

The user terminals described herein may be in the form of a mobilecommunication device (e.g., a cell phone, a VoIP equipped mobile device,etc.), laptop, tablet computer, interactive television, game console,media streaming device, head-wearable display, virtual realitydisplay/headset, augmented reality display/headset, networked watch,etc. The user terminals may optionally include displays, user inputdevices (e.g., touchscreen, keyboard, mouse, voice recognition, etc.),network interfaces, etc.

While the above detailed description has shown, described, and pointedout novel features as applied to various embodiments, it can beunderstood that various omissions, substitutions, and changes in theform and details of the devices or algorithms illustrated can be madewithout departing from the spirit of the disclosure. As can berecognized, certain embodiments described herein can be embodied withina form that does not provide all of the features and benefits set forthherein, as some features can be used or practiced separately fromothers.

What is claimed is:
 1. A content distribution system, the contentdistribution system comprising: a data repository configured to storeuploads of a plurality of media files of media submitters, including oneor more media files comprising performance data; and a computer systemconfigured to: provide access to media files of media submitters storedon the data repository to a plurality of different types of userdevices, including at least a phone, over a communication network;obtain feedback from users with respect to the media files of mediasubmitters; determine that a selected media submitter meets a thresholdvalue or condition comprising a quantity of views of content associatedwith the selected media submitter; based at least in part on: thedetermination that the selected media submitter meets the thresholdvalue or condition comprising a quantity of views of content associatedwith the selected media submitter, provide a first offer of services tothe selected media submitter; deliver media files associated with theselected media submitter and one or more advertisements to user devices;monitor subsequent user interactions with the one or more advertisementsassociated with the media files associated with the selected mediasubmitter; enable one or more services to be provided to the selectedmedia submitter; enable participation of the selected media submitter ina proceeds-sharing program based on the associated advertisements, theproceeds-sharing program having at least one proceeds calculation basedon user interaction with the associated advertisements; track downloadsof media files associated with the selected media submitter to userdevices; and provide proceeds to the selected media submitter based atleast in part on a number of downloads of media files associated withthe selected media submitter to user devices.
 2. The contentdistribution system as defined in claim 1, wherein the computer systemis further configured to determine a service level provided to a givenmedia submitter based at least in part on differently weightedinteractions related to the given media submitter's media files.
 3. Thecontent distribution system as defined in claim 1, wherein the computersystem is further configured to transmit an electronic notification toenable at least one service to be initiated.
 4. The content distributionsystem as defined in claim 1, wherein the computer system is furtherconfigured to: record instances or scores of advertisement viewings inan account of the selected media submitter; wherein proceeds provided tothe selected media submitter is determined using the recorded instancesor scores of advertisement viewings and the tracked downloads.
 5. Thecontent distribution system as defined in claim 1, wherein determiningthat the selected media submitter meets the threshold value or conditionis based at least in part on an aggregate number of views of contentassociated with the selected media submitter.
 6. The contentdistribution system as defined in claim 1, wherein the computer systemis further configured to provide access to media files stored on thedata repository to one or more user devices over the communicationnetwork via a profile page of the selected media submitter.
 7. Thecontent distribution system as defined in claim 1, wherein the computersystem is further configured to enable users to communicate with theselected media submitter via an interface provided via a profile page ofthe selected media submitter.
 8. The content distribution system asdefined in claim 1, wherein the computer system is further configured toenable users to access media files of at least one media submitter via asearch engine.
 9. The content distribution system as defined in claim 1,wherein the computer system is further configured to provide theselected media submitter online, automated media production services.10. The content distribution system as defined in claim 1, wherein thecomputer system is further configured to determine a ranking withrespect to at least one media file within a specific subject mattercategory.
 11. The content distribution system as defined in claim 1,wherein the computer system is further configured to enable uploadedmedia files to be provided to users via a third party distributionnetwork.
 12. The content distribution system as defined in claim 1,wherein the threshold value relates in part to a ranking of a submittedmedia file, comprising a performance, within a first category.
 13. Thecontent distribution system as defined in claim 1, wherein the thresholdvalue relates in part to a quantification of submitted positivefeedback.
 14. The content distribution system as defined in claim 1,wherein the computer system is configured to monitor activations relatedto content, wherein at least one service offer is provided to the mediasubmitter based at least in part on the monitored activations.
 15. Acontent distribution system, the content distribution system comprising:a data repository configured to store uploads of a plurality of mediafiles of media submitters, the plurality of media files comprisingperformance data; and a computer system configured to: provide access tothe plurality media files to a plurality of user devices; determine aquantity of views with respect to content associated with one or moremedia submitters; determine that one or more of the media submittersmeets a threshold condition using the determined quantity of views withrespect to content associated with respective media submitters; based atleast in part on the determination that one or more of the mediasubmitters meets the threshold condition using the determined quantityof views with respect to content associated with respective mediasubmitters, provide an offer of services to the one or more mediasubmitters; deliver at least one media file from the plurality of mediafiles stored in the data repository and associated with a selected mediasubmitter to at least one user device; deliver one or moreadvertisements to said at least one user device and associate saidadvertisements with the at least one media file associated with theselected media submitter; enable participation of the selected mediasubmitter in a proceeds-sharing program based on the associatedadvertisements, the proceed sharing program having at least one proceedscalculation based on user interaction with the associatedadvertisements; monitor user interactions with the one or moreadvertisements associated with the at least one media file associatedwith the selected media submitter; based at least in part on themonitored user interactions with the one or more advertisementsassociated with the at least one media file, enable the selected mediasubmitter to receive proceeds associated with user interactions with theone or more advertisements; and provide proceeds to the selected mediasubmitter based at least in part on user subscriptions.
 16. The contentdistribution system as defined in claim 15, wherein the computer systemis further configured to determine a service level provided to a givenmedia submitter based at least in part on differently weightedinteractions related to the given media submitter's media files.
 17. Thecontent distribution system as defined in claim 15, wherein the computersystem is further configured to transmit an electronic notification toenable at least one service to be initiated.
 18. The contentdistribution system as defined in claim 15, wherein the computer systemis further configured to: record instances or scores of advertisementviewings in an account of the selected media submitter; wherein proceedsprovided to the selected media submitter is determined using therecorded instances or scores of advertisement viewings.
 19. The contentdistribution system as defined in claim 15, wherein determining thatthat one or more of the media submitters meets a threshold condition isbased at least in part on an aggregate number of views of content ofrespective media submitters.
 20. The content distribution system asdefined in claim 15, wherein the computer system is further configuredto provide access to media files stored on the data repository to one ormore user devices over the communication network via a profile page ofthe selected media submitter.
 21. The content distribution system asdefined in claim 15, wherein the computer system is further configuredto enable users to communicate with at least one media submitter via aninterface provided via a profile page of the at least one mediasubmitter.
 22. The content distribution system as defined in claim 15,wherein the computer system is further configured to enable users toaccess media files of at least one media submitter via a search engine.23. The content distribution system as defined in claim 15, wherein thecomputer system is further configured to provide the selected mediasubmitter online, automated media production services.
 24. The contentdistribution system as defined in claim 15, wherein the computer systemis further configured to determine a ranking with respect to at leastone media file within a specific subject matter category.
 25. Thecontent distribution system as defined in claim 15, wherein the computersystem is further configured to enable uploaded media files to beprovided to users via a third party distribution network.
 26. Thecontent distribution system as defined in claim 15, wherein thethreshold condition relates, in part, to a ranking of a submitted mediafile, comprising a performance, within a first category.
 27. The contentdistribution system as defined in claim 15, wherein the thresholdcondition relates, in part, to a quantification of submitted positivefeedback.
 28. The content distribution system as defined in claim 15,wherein the computer system is configured to monitor activations relatedto content, wherein at least one service offer is provided to theselected media submitter based at least in part on the monitoredactivations.